Expert knowledge for the recognition of leukemic cells

This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image recognition are needed to achieve an adequate identification of blood tissues. This paper presents a procedure to acquire expert knowledge from blood cell images. We apply Gaussian mixtures, evolutionary computing, and standard techniques of image processing to extract knowledge. This information feeds a support vector machine or multilayer perceptron to classify healthy or leukemic cells. Additionally, convolutional neural networks are used as a benchmark to compare our proposed method with the state of the art. We use a public database of 260 healthy and leukemic cell images. Results show that our traditional pattern recognition methodology matches deep learning accuracy since the recognition of blood cells achieves 99.63%, whereas the convolutional neural networks reach 97.74% on average. Moreover, the computational effort of our approach is minimal, while meeting the requirement of being explainable.